Quantifying and estimating the predictive accuracy for censored time-to-event data with competing risks.
This paper focuses on quantifying and estimating the predictive accuracy of prognostic models for time-to-event outcomes with competing events. We consider the time-dependent discrimination and calibration metrics, including the receiver operating characteristics curve and the Brier score, in the co...
| Publicado en: | Statistics in Medicine Vol. 37; no. 21; pp. 3106 - 3125 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
9/20/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131260821&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131260821 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: 9/20/2018 vid: 37 iid: 21 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 131260821 131260821 NLM29766537 131260821 10.1002/sim.7806 NLM29766537 131260821 ppf: 3106 ppct: 19 formats: tig: atl: Quantifying and estimating the predictive accuracy for censored time-to-event data with competing risks. aug: au: Wu, Cai Li, Liang affil: Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, TX, USA sug: subj: Models, Statistical Nonparametric Statistics Computer Simulation Predictive Value of Tests Time Factors Kidney Failure, Chronic Mortality Probability ROC Curve African Americans Statistics and Numerical Data Prognosis Questionnaires Human ab: This paper focuses on quantifying and estimating the predictive accuracy of prognostic models for time-to-event outcomes with competing events. We consider the time-dependent discrimination and calibration metrics, including the receiver operating characteristics curve and the Brier score, in the context of competing risks. To address censoring, we propose a unified nonparametric estimation framework for both discrimination and calibration measures, by weighting the censored subjects with the conditional probability of the event of interest given the observed data. The proposed method can be extended to time-dependent predictive accuracy metrics constructed from a general class of loss functions. We apply the methodology to a data set from the African American Study of Kidney Disease and Hypertension to evaluate the predictive accuracy of a prognostic risk score in predicting end-stage renal disease, accounting for the competing risk of pre-end-stage renal disease death, and evaluate its numerical performance in extensive simulation studies. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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